1932

Abstract

Many communities in the United States are struggling to deal with the negative consequences of illicit opioid use. Effectively addressing this epidemic requires the coordination and support of community stakeholders in a change process with common goals and objectives, continuous engagement with individuals with opioid use disorder (OUD) through their treatment and recovery journeys, application of systems engineering principles to drive process change and sustain it, and use of a formal evaluation process to support a learning community that continuously adapts. This review presents strategies to improve OUD treatment and recovery with a focus on engineering approaches grounded in systems thinking.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-082719-040832
2020-06-04
2024-04-24
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/22/1/annurev-bioeng-082719-040832.html?itemId=/content/journals/10.1146/annurev-bioeng-082719-040832&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Scholl L, Seth P, Kariisa M, Wilson N, Baldwin G 2019. Drug and opioid-involved overdose deaths—United States, 2013–2017. Morb. Mortal. Wkly. Rep. Surveill. Summ. 67:1419–27
    [Google Scholar]
  2. 2. 
    CDC (Cent. Dis. Control Prev.) 2019. Multiple Cause of Death 19992018 Atlanta: CDC/Natl. Cent. Health Stat https://wonder.cdc.gov/wonder/help/mcd.html
    [Google Scholar]
  3. 3. 
    Litton S. 2018. Economic toll of opioid crisis in the U.S. exceeded $1 trillion since 2001. Altarum Febr. 13. https://altarum.org/news/economic-toll-opioid-crisis-us-exceeded-1-trillion-2001
    [Google Scholar]
  4. 4. 
    Higham S, Horwitz S, Rich S 2019. 76 billion opioid pills: Newly released federal data unmasks the epidemic. Washington Post July 16. https://www.washingtonpost.com/investigations/76-billion-opioid-pills-newly-released-federal-data-unmasks-the-epidemic/2019/07/16/5f29fd62-a73e-11e9-86dd-d7f0e60391e9_story.html?utm_term=.02f38eb66461
    [Google Scholar]
  5. 5. 
    CDC (Cent. Dis. Control Prev.) 2019. U.S. Opioid Prescribing Rate Maps. Atlanta: CDC https://www.cdc.gov/drugoverdose/maps/rxrate-maps.html
  6. 6. 
    Metraux S, Culhane DP. 2006. Recent incarceration history among a sheltered homeless population. Crime Delinq 52:504–17
    [Google Scholar]
  7. 7. 
    Miller-Archie SA, Walters SC, Singh TP, Lim S 2019. Impact of supportive housing on substance use–related health care utilization among homeless persons who are active substance users. Ann. Epidemiol. 32:1–6
    [Google Scholar]
  8. 8. 
    Bronson J, Stroop J, Zimmer S, Berzofsky M 2017. Drug Use, Dependence, and Abuse Among State Prisoners and Jail Inmates, 2007–2009 Washington, DC: Off. Juv. Justice Delinq. Prev., Dep. Justice
  9. 9. 
    Chamberlain A, Nyamu S, Aminawung J, Wang EA, Shavit S, Fox AD 2019. Illicit substance use after release from prison among formerly incarcerated primary care patients: a cross-sectional study. Addict. Sci. Clin. Pract. 14:7
    [Google Scholar]
  10. 10. 
    Owens MD, Chen JA, Simpson TL, Timko C, Williams EC 2018. Barriers to addiction treatment among formerly incarcerated adults with substance use disorders. Addict. Sci. Clin. Pract. 13:19
    [Google Scholar]
  11. 11. 
    Lauritzen G. 2018. Changes in opiate and stimulant use through 10 years: the role of contextual factors, mental health disorders and psychosocial factors in a prospective treatment cohort study. PLOS ONE 13:e0190381
    [Google Scholar]
  12. 12. 
    Dasgupta N, Beletsky L, Ciccarone D 2018. Opioid crisis: no easy fix to its social and economic determinants. Am. J. Public Health 108:182–86
    [Google Scholar]
  13. 13. 
    Terry N. 2019. Structural determinism amplifying the opioid crisis: It's the healthcare, stupid. ! Northwest. Univ. Law Rev. 11:315
    [Google Scholar]
  14. 14. 
    Sandoe E, Fry CE, Frank RG 2018. Policy levers that states can use to improve opioid addiction treatment and address the opioid epidemic. Health Affairs Blog Oct. 2. https://www.healthaffairs.org/do/10.1377/hblog20180927.51221/full/
    [Google Scholar]
  15. 15. 
    Ornstein C. 2018. Measuring the toll of the opioid epidemic is tougher than it seems. ProPublica March 13. https://www.propublica.org/article/measuring-the-toll-of-the-opioid-epidemic-is-tougher-than-it-seems
    [Google Scholar]
  16. 16. 
    Klimas J, Gorfinkel KJ, Fairbairn N, Amato L, Ahamad K et al. 2019. Strategies to identify patient risks of prescription opioid addiction when initiating opioids for pain: a systematic review. JAMA Netw. Open 2:e193365
    [Google Scholar]
  17. 17. 
    Ruhm CJ. 2018. Corrected US opioid-involved drug poisoning deaths and mortality rates, 1999–2015. Addiction 133:1339–44
    [Google Scholar]
  18. 18. 
    Bubka KM, Brent J, Juurlink DN 2019. Prevention of opioid overdose. N. Engl. J. Med. 380:2246–55
    [Google Scholar]
  19. 19. 
    Radomski TR, Bixler FR, Zickmund SL, Roman KM, Thorpe CT et al. 2018. Physicians’ perspectives regarding prescription drug monitoring program use within the Department of Veterans Affairs: a multi-state qualitative study. J. Gen. Intern. Med. 33:1253–59
    [Google Scholar]
  20. 20. 
    Mallow PJ, Sathe N, Topmiller M, Chubinski J, Carr D, Christopher R 2019. Estimating the prevalence of opioid use disorder in the Cincinnati region using probabilistic multiplier methods and model averaging. J. Health Econ. Outcomes Res. 6:61–69
    [Google Scholar]
  21. 21. 
    Fischer B, Varatharajan T, Shield K, Rehm J, Jones W 2018. Crude estimates of prescription opioid–related misuse and use disorder populations towards informing intervention system need in Canada. Drug Alcohol Depend 189:76–79
    [Google Scholar]
  22. 22. 
    Rao JNK, Molina I. 2015. Small Area Estimation Hoboken, NJ: Wiley, 2nd ed..
  23. 23. 
    Bearnot B, Pearson JF, Rodriguez JA 2018. Using publicly available data to understand the opioid overdose epidemic: geospatial distribution of discarded needles in Boston, Massachusetts. Am. J. Public Health 108:1355–57
    [Google Scholar]
  24. 24. 
    Ciesielski T, Iyengar R, Bothra A, Tomala D, Cislo G, Gage BF 2016. A tool to assess risk of de novo opioid abuse or dependence. Am. J. Med. 129:699–705
    [Google Scholar]
  25. 25. 
    Cochran BN, Flentje A, Heck NC, Van Den Bos J, Perlman D 2014. Factors predicting development of opioid use disorders among individuals who receive an initial opioid prescription: mathematical modeling using a database of commercially-insured individuals. Drug Alcohol Depend 138:202–8
    [Google Scholar]
  26. 26. 
    Lo-Ciganic W-H, Huang JL, Zhang HH 2019. Evaluation of machine-learning algorithms for predicting overdose risk among Medicare beneficiaries with opioid prescriptions. JAMA Netw. Open 2:e190968
    [Google Scholar]
  27. 27. 
    Ellis RJ, Wang Z, Genes N, Ma'ayn A 2019. Predicting opioid dependence from electronic health records with machine learning. BioData Min 12:3
    [Google Scholar]
  28. 28. 
    Chaudhary MA, Bulani N, deJager EC, Lipsitz S, Kwon NK et al. 2019. Development and validation of bedside risk assessment for sustaining prescription opioid use after surgery. JAMA Netw. Open 2:e196673
    [Google Scholar]
  29. 29. 
    Char DS, Shah NH, Magnus D 2018. Implementing machine learning in health care—addressing ethical challenges. N. Engl. J. Med. 378:981–83
    [Google Scholar]
  30. 30. 
    Harris AHS. 2019. Three critical questions that should be asked before using prediction models for clinical decision support. JAMA Netw. Open 2:e196661
    [Google Scholar]
  31. 31. 
    Battista NA, Pearcy LB, Stickland WC 2019. Modeling the prescription opioid epidemic. Bull. Math. Biol. 81:2258–89
    [Google Scholar]
  32. 32. 
    Pitt AL, Humphreys K, Brandeau ML 2018. Modeling health benefits and harms of public policy responses to the US opioid epidemic. Am. J. Public Health 108:1394–400
    [Google Scholar]
  33. 33. 
    Chen Q, Larochelle MR, Weaver DT, Lietz AP, Mueller PP et al. 2019. Prevention of prescription opioid misuse and projected overdose deaths in the United States. JAMA Netw. Open 2:e187621
    [Google Scholar]
  34. 34. 
    López-Soto D, Griffin PM. 2019. Estimating the impact of provider-based interventions on neonatal abstinence syndrome Work. Pap., Purdue Univ., West Lafayette, IN
  35. 35. 
    Winkelman TNA, Villapiano N, Kozhimannil KB, Davis MM, Patrick SW 2018. Incidence and costs of neonatal abstinence syndrome among infants with Medicaid: 2004–2014. Pediatrica 141:e20173520
    [Google Scholar]
  36. 36. 
    Bobashev G, Goree S, Frank J, Zule W 2018. Pain Town, an agent-based model of opioid use trajectories in a small community. Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2018): Social, Cultural, and Behavioral Modeling R Thomson, C Dancy, A Hyder, H Bisgin 274–85 Cham, Switz: Springer
    [Google Scholar]
  37. 37. 
    Nelson AE. 2018. Quantifying spatial potential access equity in an agent based simulation model of buprenorphine treatment policy in the United States PhD Thesis, Pap. 4516, Portland State Univ., Portland, OR. https://pdxscholar.library.pdx.edu/open_access_etds/4516
  38. 38. 
    Zin L, Havens JR, Rudolph AE, Young AM, Yazdi GE et al. 2019. An agent-based network model of hepatitis C virus transmission among people who inject drugs calibrated to a high-burden rural population in the United States Poster 4-56 presented at 41st Annual Meeting of the Society for Medical Decision Making Portland: OR, Oct 20–23
  39. 39. 
    Goeva A, Lam H, Qian H, Zhang B 2019. Optimization-based calibration of simulation input models. Oper. Res. 67: https://doi.org/10.1287/opre.2018.1801
    [Crossref] [Google Scholar]
  40. 40. 
    Cosenza B, Popov N, Juulink B, Richmond P, Chimeh MK et al. 2018. OpenABL: a domain-specific language for parallel and distributed agent-based simulations. Proceedings of the European Conference on Parallel Processing505–18 Cham, Switz: Springer
    [Google Scholar]
  41. 41. 
    Fry CE, Nikpay SS, Leslie E, Buntin MB 2018. Evaluating community-based health improvement programs. Health Aff 37:22–29
    [Google Scholar]
  42. 42. 
    Wing C, Simon K, Bello-Gomez RA 2018. Designing difference in difference studies: best practices for health policy research. Annu. Rev. Public Health 39:453–69
    [Google Scholar]
  43. 43. 
    Dowell D, Haegerich T, Chou R 2019. No shortcuts to safer opioid prescribing. N. Engl. J. Med. 380:2285–87
    [Google Scholar]
  44. 44. 
    Keast SL, Kim H, Deyo RA, Middleton L, McConnell KJ et al. 2018. Effects of a prior authorization policy for extended-release/long acting opioids on utilization and outcomes in a state Medicaid program. Addiction 113:1651–60
    [Google Scholar]
  45. 45. 
    Sharp A, Jones A, Sherwood J, Kutsa O, Honermann B, Millett G 2018. Impact of Medicaid expansion on access to opioid analgesic medications and medication-assisted treatment. Am. J. Public Health 108:642–48
    [Google Scholar]
  46. 46. 
    Binswanger IA, Koester S, Mueller SR, Gardner EM, Goddard K, Glanz JM 2015. Overdose education and naloxone for patients prescribed opioids in primary care: a qualitative study of primary care staff. J. Gen. Intern. Med. 30:1837–44
    [Google Scholar]
  47. 47. 
    Carey RG, Lloyd RC. 2001. Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Milwaukee, WI: ASQ
  48. 48. 
    Weiner J, Murphy SM, Behrends C 2019. Expanding access to naloxone: a review of distribution strategies Issue Brief 23-132, Leonard Davis Inst. Health Econ., Univ. Pa Philadelphia: https://repository.upenn.edu/ldi_issuebriefs/132
  49. 49. 
    Raffel KE, Beach LY, Lin J, Berchuck JE, Abram S et al 2018. Naloxone distribution and training for patients with high-risk opioid use in a Veterans Affairs community-based primary care clinic. Perm. J. 22:17–179
    [Google Scholar]
  50. 50. 
    Behar E, Bagnulo ER, Coffin PO 2018. Acceptability and feasibility of naloxone prescribing in primary care settings: a systematic review. Prev. Med. 114:79–87
    [Google Scholar]
  51. 51. 
    Follman S, Arora VM, Lyttle C, Moore PQ, Pho MT 2019. Naloxone prescriptions among commercially insured individuals at high risk of opioid overdose. JAMA Netw. Open 2:e193209
    [Google Scholar]
  52. 52. 
    Curtis M, Dietze P, Aitken C, Kirwan A, Kinner SA et al. 2018. Acceptability of prison-based take-home naloxone programmes among a cohort of incarcerated men with a history of regular injecting drug use. Harm Reduct. J. 15:48
    [Google Scholar]
  53. 53. 
    Nandakumar R, Gollakota S, Sunshine JE 2019. Opioid overdose detection using smartphones. Sci. Transl. Med. 474:eaau8914
    [Google Scholar]
  54. 54. 
    Linnes JC, Hoilett OS, Twibell A, Lee H, Srivastava R et al. 2019. Methods for detecting heart rate respiration, and oxygen saturation and uses thereof US Patent Appl. 2019/0110745A1
  55. 55. 
    Dhowan B, Lim J, MacLean MD, Berman AG, Kim MK et al. 2019. Simple minimally-invasive Automatic Antidote Delivery Device (A2D2) towards closed-loop reversal of opioid overdose. J. Control. Release 308:130–37
    [Google Scholar]
  56. 56. 
    Sordo L, Barrio G, Bravo MJ, Indave BI, Degenhardt L et al. 2017. Mortality risk during and after opioid substitution treatment: systematic review and meta-analysis of cohort studies. BMJ 357:j1550
    [Google Scholar]
  57. 57. 
    Murphy SM, Polsky D, Lee JD, Friedmann PD, Kinlock TW et al. 2017. Cost-effectiveness of extended release naltrexone to prevent relapse among criminal justice–involved individuals with a history of opioid use disorder. Addiction 112:1440–50
    [Google Scholar]
  58. 58. 
    Schuckit MA. 2016. Treatment of opioid-use disorders. N. Engl. J. Med. 375:357–68
    [Google Scholar]
  59. 59. 
    Portier C, Leprevote V, Dubois-Arber F, Cottencin O, Rolland B 2014. Supervised injection services: What has been demonstrated? A systematic literature review. Drug Alcohol Depend 145:48–68
    [Google Scholar]
  60. 60. 
    Beattie M, Hookway G, Perera M, Calder S, Hunter-Rowe C et al. 2018. Improving wait time from referral to opiate replacement therapy in a drug recovery service. BMJ Open Qual 7:e000295
    [Google Scholar]
  61. 61. 
    Natl. Counc. Community Behav. Healthc 2013. Access Redesign Quality Improvement Initiative Final Rep., Natl. Counc Washington, DC: https://www.thenationalcouncil.org/wp-content/uploads/2013/01/Access-Redesign-Final-Report.pdf
  62. 62. 
    Blevins CE, Rawat N, Stein MD 2018. Gaps in the substance use disorder treatment referral process. J. Addict. Med. 12:273–77
    [Google Scholar]
  63. 63. 
    Parsch J. 2018. Wait list revisited: how a northwest Indiana behavioral healthcare provider used lean to increase capacity News Release, Novemb. 29. https://pha.purdue.edu/news-folder/wait-list-revisited-how-a-northwest-indiana-behavioral-healthcare-provider-used-lean-to-increase-capacity/
  64. 64. 
    Murray M, Berwick DM. 2003. Advanced access: reducing waiting and delays in primary care. JAMA 289:1035–40
    [Google Scholar]
  65. 65. 
    Finkelstein SR, Liu N, Rosenthal D, Lusine P 2018. When open access might not work: understanding patient attitudes in appointment scheduling. Health Care Manag. Rev. 43:348–58
    [Google Scholar]
  66. 66. 
    Srinivas S, Khasawneh MT. 2017. Design and analysis of a hybrid appointment system for patient scheduling: an optimisation approach. Int. J. Oper. Res. 29:376–99
    [Google Scholar]
  67. 67. 
    Arora S, Geppert CA, Kalishman, Dion D, Pullara F et al. 2007. Academic health center management of chronic diseases through knowledge networks: Project ECHO. Acad. Med. 82:154–60
    [Google Scholar]
  68. 68. 
    George J. 2018. Why do so few docs have buprenorphine waivers. MedPage Today Febr. 14. https://www.medpagetoday.com/psychiatry/addictions/71169
    [Google Scholar]
  69. 69. 
    Bell J, Strang J. 2020. Medication treatment of opioid use disorder. Biol. Psychiatry 87:82–88
    [Google Scholar]
  70. 70. 
    Smedslund G, Berg RC, Hammerstrom KT, Steiro A, Leiknes K et al. 2011. Motivational interviewing for substance abuse. Cochrane Syst. Rev. 11:CD008063
    [Google Scholar]
  71. 71. 
    Reif S, Braude L, Lyman DR, Dougherty RH, Daniels AS et al. 2014. Peer recovery support for individuals with substance use disorders. Psychiatr. Serv. 65:853–61
    [Google Scholar]
  72. 72. 
    Bassuk EL, Hanson J, Greene RN, Richard M, Laudet A 2016. Peer-delivered recovery support services for addictions in the United States: a systematic review. J. Subst. Abuse Treat. 63:1–9
    [Google Scholar]
  73. 73. 
    Eddie D, Hoffman L, Vilsaint C, Abry A, Bergman B et al. 2019. Lived experience in new models of care for substance use disorder: a systematic review of peer recovery support services and recovery coaching. Front. Psychol. 10:1052
    [Google Scholar]
  74. 74. 
    Jack HE, Oller D, Kelly J, Magidson JF, Wakeman SE 2017. Addressing substance use disorder in primary care: the role, integration, and impact of recovery coaches. Subst. Abuse 39:307–14
    [Google Scholar]
  75. 75. 
    Samuels EA, Baird J, Yang ES, Mello MJ 2018. Adoption and utilization of an emergency department naloxone distribution and peer recovery coach consultation program. Acad. Emerg. Med. 26:160–73
    [Google Scholar]
  76. 76. 
    Scott CK, Grella CE, Nicholson L, Dennis ML 2018. Opioid recovery initiation: pilot test of a peer outreach and modified recovery management checkup intervention for out-of-treatment opioid users. J. Subst. Abuse Treat. 86:30–35
    [Google Scholar]
  77. 77. 
    Schwartz RP, Kelly SM, O'Grady K, Brown BS 2009. Attitudes toward buprenorphine and methadone among opioid-dependent individuals. Am. J. Addict. 17:396–401
    [Google Scholar]
  78. 78. 
    Gagne CA, Finch WL, Myrick KJ, Davis LM 2018. Peer workers in the behavioral and integrated health workforce: opportunities and future directions. Am. J. Prev. Med. 54:S258–66
    [Google Scholar]
  79. 79. 
    Schuman-Olivier Z, Borodovsky JT, Steinkamp J, Muir Q, Butler K et al. 2018. MySafeRx: a mobile technology platform integrating motivational coaching, adherence monitoring, and electronic pill dispensing for enhancing buprenorphine/naloxone adherence during opioid use disorder treatment: a pilot study. Addict. Sci. Clin. Pract. 31:21
    [Google Scholar]
  80. 80. 
    Baxter JD, Clark RE, Samnaliev M, Aweh G, O'Connell E 2014. Adherence to buprenorphine treatment guidelines in a Medicaid program. Subst. Abuse 36:174–82
    [Google Scholar]
  81. 81. 
    Watz E. 2018. Pear approval signals FDA readiness for digital treatments. Nat. Biotechnol. 36:481–82
    [Google Scholar]
  82. 82. 
    Tofighi B, Chemi C, Ruiz-Valcarcel J, Hein P, Hu L 2019. Smartphone apps targeting alcohol and illicit substance use: systematic search in commercial app stores and critical content analysis. JMIR mHealth uHealth 7:e11831
    [Google Scholar]
  83. 83. 
    Hedberg K, Bui L, Livingston C, Sheilds L, Van Otterloo J 2019. Integrating public health and health care strategies to address the opioid epidemic: the Oregon Health Authority's opioid initiative. J. Public Health Manag. Pract. 25:214–20
    [Google Scholar]
  84. 84. 
    Miliard M. 2019. Clinicians need better opioid data within their workflows, says EHRA. Healthcare IT News Febr. 22. https://www.healthcareitnews.com/news/clinicians-need-better-opioid-data-within-their-workflows-says-ehra
    [Google Scholar]
  85. 85. 
    van Panhuis WG, Paul P, Emerson C, Grefenstette J, Wilder R et al. 2014. Systematic review of barriers to datasharing in public health. BMC Public Health 14:1144
    [Google Scholar]
  86. 86. 
    Allen C, Des Jardins TR, Heider A, Lyman KA, McWilliams L et al. 2014. Data governance and data sharing agreement for community-wide health information exchange: lessons from the beacon communities. J. Electron. Health Data Methods 2:1057
    [Google Scholar]
  87. 87. 
    Smart JC. 2016. Technology for privacy assurance. Ethical Reasoning in Big Data J Collmann, S Matei 93–114 Cham, Switz: Springer
    [Google Scholar]
  88. 88. 
    Agbo C, Mahmoud QH, Eklund JM 2019. Blockchain technology in healthcare: a systematic review. Healthcare 7:e56
    [Google Scholar]
  89. 89. 
    Kaur H, Alam MA, Jameel R, Kumar Mourya A, Chang V et al. 2018. A proposed solution and future direction for blockchain-based heterogeneous Medicare data in cloud environment. J. Med. Syst. 42:156
    [Google Scholar]
  90. 90. 
    Strickland JC, Stoops WW. 2019. The use of crowdsourcing in addiction science research: Amazon Mechanical Turk. Exp. Clin. Psychopharmacol. 27:1–18
    [Google Scholar]
  91. 91. 
    Wazny K. 2018. Applications of crowdsourcing in health: an overview. J. Glob. Health 8:010502
    [Google Scholar]
  92. 92. 
    Vaish R, Wyngarden K, Chen J, Cheung B, Bernstein MS 2014. Twitch crowdsourcing: crowd contributions in short bursts of time. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems3645–54 New York: ACM
    [Google Scholar]
  93. 93. 
    Chiang C-W, Kasunic A, Savage S 2018. Crowd coach: peer coaching for crowd workers’ skill growth. arXiv:1811.05364v1 [cs]
  94. 94. 
    Mandl KD, Kohane IS, McFadden D, Weber GM, Natter M et al. 2014. Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture. JAMA 21:615–20
    [Google Scholar]
  95. 95. 
    NIDA (Natl. Inst. Drug Addict.) 2016. Advancing addiction science 2016–2020 Strateg. Plan, NIDA Washington, DC: https://d14rmgtrwzf5a.cloudfront.net/sites/default/files/nida_2016strategicplan_032316.pdf
  96. 96. 
    Morrison E, Hutcheson S, Nilsen E, Fadden J, Franklin N 2019. Strategic Doing: Ten Skills for Agile Leadership Hoboken, NJ: Wiley
  97. 97. 
    Brason F. 2013. Project Lazarus: an innovative community response to prescription drug overdose. N. C. Med. J. 74:259–61
    [Google Scholar]
  98. 98. 
    Glandon D, Paina L, Alonge O, Peters DH, Bennett S 2017. 10 best resources for community engagement in implementation research. Health Policy Plan 32:1457–65
    [Google Scholar]
  99. 99. 
    Embi PJ, Payne PR. 2013. Evidence generating medicine: redefining the research–practice relationship to complete the evidence cycle. Med. Care 51:S87–91
    [Google Scholar]
  100. 100. 
    Smoyer WE, Embi PJ, Moffatt-Bruce S 2016. Creating local learning health systems: Think globally, act locally. JAMA 316:2481–82
    [Google Scholar]
  101. 101. 
    Budrionis A, Bellika JG. 2016. The learning healthcare system: Where are we now? A systematic review. J. Biomed. Inf. 64:87–92
    [Google Scholar]
  102. 102. 
    Mack D, Zhang S, Douglas M, Sow C, Strothers H, Rust G 2016. Disparities in primary care EHR adoption rates. J. Health Care Poor Underserved 27:327–38
    [Google Scholar]
  103. 103. 
    Mullins CD, Wingate LMT, Edwards HA, Tofade T, Wutoh A 2018. Transitioning from learning healthcare systems to learning health care communities. J. Comp. Eff. Res. 7:603–14
    [Google Scholar]
  104. 104. 
    Balasubramanian BA, Cohen DJ, Davis MM, Gunn R, Dickinson LM et al. 2015. Learning evaluation: blending quality improvement and implementation research methods to study healthcare innovations. Implement. Sci. 10:31
    [Google Scholar]
  105. 105. 
    Rojas E, Munoz-Gama J, Sepulveda M, Capurro D 2016. Process mining in healthcare: a literature review. J. Biomed. Inf. 61:224–36
    [Google Scholar]
  106. 106. 
    Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M et al. 2019. A guide to deep learning in healthcare. Nat. Med. 25:24–29
    [Google Scholar]
/content/journals/10.1146/annurev-bioeng-082719-040832
Loading
/content/journals/10.1146/annurev-bioeng-082719-040832
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error